Implementation of novel deep learning algorithms for the high-resolution study of the lacuno-canalicular network from individuals with a documented history of chronic opioid Use

Taylor, Joshua Thomas (2024) Implementation of novel deep learning algorithms for the high-resolution study of the lacuno-canalicular network from individuals with a documented history of chronic opioid Use. Masters thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (3MB)

Abstract

Bone is a dynamic tissue that changes throughout life. This process is governed by osteocytes that exist in a lacuno-canalicular network (LCN), but is altered by several factors, including exercise, age, nutrition, and substance use. Artificial intelligence brought several enhancements to image segmentation for medical imaging. However, it has not been applied to study the LCN in human bone. This thesis implements novel deep learning methods on Synchrotron Radiation micro-Computed Tomography (SRμCT) datasets of human rib cortical bone microstructure to characterize osteoporosis-related features. Ninety-seven human left sixth rib specimens (male: n = 60, female n = 37) were excised from cadavers with informed consent. The specimens were divided into age categories defined by decade. A 50-slice subset from six samples was segmented to train the U-Net++ deep learning model. It was compared to traditional and manual segmentation methods. Deep learning performed comparably to the traditional method, although it was more time-efficient. A follow-up model with the MA-Net architecture more accurately segmented the data. Comparing segmented microstructural parameters with opioid use, sex, and age revealed age as the most significant predictor of deteriorating bone health. The results did not provide strong evidence of drug-induced impacts on bone health as originally predicted, however, there are some indications hinting at a link between opioid use and bone health. A follow-up study implementing a rabbit model is underway to eliminate confounding factors present in a human population. However, this project successfully created a novel segmentation algorithm that performed more efficiently in SRμCT data segmentation.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/16691
Item ID: 16691
Additional Information: Includes bibliographical references (pages 111-131)
Keywords: bone, synchrotron, opioids, artifical intelligence, cortical porosity
Department(s): Medicine, Faculty of > Biomedical Sciences
Date: October 2024
Date Type: Submission
Medical Subject Heading: Cortical Bone; Synchrotrons; Artificial Intelligence; Deep Learning; Analgesics, Opioid; Osteocytes; Osteoporosis

Actions (login required)

View Item View Item

Downloads

Downloads per month over the past year

View more statistics